Open-Source ML Is at It...

Open-source ML is at it again! Introducing ColossalChat - an open-source solution for cloning ChatGPT with a complete RLHF pipeline. Here is what you need to know: View Tweet

While LLMs like ChatGPT are available as a service, we need a practical open-source solution with a complete RLHF pipeline. Colossal-AI presents ColossalChat, a new open-source solution built upon the LLaMA model that closely resembles the original ChatGPT technical solution. View Tweet

ColossalChat requires less than 10B parameters and can attain bilingual proficiency in English & Chinese through RLHF finetuning. Achieves comparable results to ChatGPT and GPT-3.5. The examples showcase email writing and code generation capabilities. https://t.co/iMap9TMDIi View Tweet

ColossalChat also releases a bilingual dataset with 100K Q&A pairs in both English & Chinese. It’s collected from real-life question scenarios and expanded using self-instruct technology. It contains realistic and diverse seed data and is suitable for both fine-tuning and RLHF. View Tweet

Stanford Alpaca, based on LLaMA, provides a good glimpse of how to improve LLMs through supervised fine-tuning at a fraction of the cost. These solutions don’t apply RLHF like ChatGPT does and they’re missing subsequent alignment and RL tuning. They’re also limited to English. View Tweet

ColossalChat includes the RLHF process that aims to replicate ChatGPT-like models. ColossalChat RLHF pipeline involves three stages: supervised instruct fine-tuning, training a reward model, and optimizing with reinforcement learning. View Tweet

Getting started with ColossalChat is simple. You can run the RLHF training process with the Colossal-AI library as follows: https://t.co/Ll9gteitrb View Tweet

Once the fine-tuned model weights are obtained, quantization can be applied to reduce hardware costs for inference. This allows for online inference services to be launched requiring only a single GPU with ~4GB memory to deploy the 7B parameter model inference service. View Tweet

Find more of the technical details in the blog post: https://t.co/aUhar3rBeZ Checkout the Colossal-AI open-source library here: https://t.co/vfwwDciLZ2 Join the Colossal-AI Slack here: https://t.co/FJ7F0WDuMZ View Tweet